(Gen) AI for Motivation and Engagement

Theories and Tools Supporting Learners’ Engagement and Motivation through Artificial Intelligence and Generative AI

The aim of this special issue is to gather research that investigates how Artificial Intelligence (AI) tools, including but not limited to Large Language Models and Generative AI (GenAI), can support learners’ Motivation and Engagement in ways that enhance learning.

We welcome studies on individual and collaborative learning, on formal and informal educational settings, and on a wide range of pedagogical uses of AI, provided that they engage seriously with the educational mechanisms involved.

In doing so, the special issue seeks not merely to document technological novelty, but to advance a deeper understanding of how AI may contribute to more meaningful, sustained, and effective learning experiences.

In the opinion of the Guest Editors, this special issue is timely; we discuss why in the following, after we have given a description of the evolving landscape of Research on Motivation and Engagement.



Motivation and Engagement have long been central constructs in education because they help explain why some learners persist, invest effort, and participate deeply in learning, while others disengage, withdraw, or achieve below their potential.

Although the two concepts are related, they are not identical. Engagement is commonly understood as a multidimensional construct involving behavioral participation, emotional involvement, and cognitive investment, while motivation concerns the processes and reasons that initiate, direct, and sustain learning activity.

Together, these constructs are foundational for understanding learning success, in both individual and collaborative learning.

We know that research on these constructs dates back from... long time.

Way before the application of AI techniques, longitudinal and meta-analytic studies show that motivation and academic achievement are reciprocally related, and that specific forms of motivation, especially self-beliefs and autonomous motivation, are more strongly associated with later achievement than externally controlled motivation.

Engagement, as well, is positively associated with learning outcomes; and motivation often affects learning partly through engagement.



On the other hand, AI in Technology-Enhanced Learning has provided a wealth of results on the support to learning, beyond motivation and engagement.

The support of AI to educational activities has been shown of educational value in several areas, such as personalized/adaptive learning, intelligent tutoring, feedback, peer assessment, and more, showing that it is at its strongest when it supports personalization, timely feedback, and pedagogically structured interaction rather than mere automation.

Other recent studies show positive effects of the use of GenAI on behavioral, cognitive, and emotional engagement, and in many cases improve affective-motivational states as well.

However, there are caveats: in some settings, GenAI may reduce active learning behaviors or weaken learning motivation, especially when challenge, support, and instructional design are poorly balanced.

The central question, now, is no longer whether AI can influence learners’ Motivation and Engagement, but how, under what conditions, and with what consequences for learning this can happen.

The most promising studies increasingly examine mediating processes such as autonomy support, self-regulation, feedback quality, task challenge, pedagogical scaffolding, and the interaction between learners and AI systems.

So, the field is now at an ideal point for a special issue.

There is enough research to support substantive scholarly dialogue, but not enough conceptual convergence to claim that the topic is mature or exhausted.



Much of the existing literature is concentrated in higher education, especially in language-related contexts; many studies are short-term; many rely heavily on self-report (perception).

What about the difference between autonomous and controlled motivation? Or between visible participation and deep engagement, or between temporary novelty effects and durable educational change?

These gaps indicate that a focused venue is needed for work that is theoretically grounded, methodologically robust, and educationally consequential.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI) in Education, Generative AI (GenAI), Learner Motivation, Student Engagement, Technology-Enhanced Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.